适应性单一光谱分解混合框架,用于风力发电预测的二次误差校正
Chunliang Mai1,2, Lixin Zhang3,4, Omar Behar5
1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
iScience
|May 5, 2025
概括
这项研究引入了一种用于准确预测风力发电的新型混合模型,大大提高了预测准确度并减少了错误. 这种先进的方法增强了可再生能源在电网中的整合.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
- 电力系统的信号处理.
背景情况:
- 准确的风能预测对于电网稳定性和高效的可再生能源整合至关重要.
- 风力数据的固有非线性和随机性质带来了重大的预测挑战.
- 现有的预测模型经常在降噪和超参数优化方面扎.
研究的目的:
- 开发一个强大而准确的混合模型,用于高精度的风力发电预测.
- 为应对风力数据复杂性带来的挑战,提高预测可靠性.
- 通过先进的预测技术,加强风能与电网的整合.
主要方法:
- 适应性改进单一频谱分析 (ISSA) 用于无参数的降噪和模式分解.
- 优化双向时间卷积网络-双向长期短期记忆 (BiTCN-BiLSTM) 网络.
- AdaBoost 组合学习用于动态错误校正和增强的稳定性.
- 混合策略增强的甲虫优化 (OTDBO) 用于超参数调整.
主要成果:
- 在Dabancheng风电场的季节性数据集上显示了显著的性能改进.
- 平均绝对误差 (MAE) 降低了45.4%,平方根平均误差 (RMSE) 降低了47.6% (p < 0.001).
- 培训时间减少了12.1%-21.3%,表明计算效率.
结论:
- 拟议的混合模型在风力发电预测准确性和稳定性方面取得了重大进展.
- 这种方法提供了一个可扩展的解决方案,可靠地将可再生能源整合到电网中.
- ISSA,BiTCN-BiLSTM和AdaBoost的组合有效地处理复杂的风数据特征.
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